class_docstr: Gaussian head with state-independent learned covariance.
  
  This link is intended to be attached to a neural network that outputs
  the mean of a Gaussian policy. The only learnable parameter this link has
  determines the variance in a state-independent way.
  
  State-independent parameterization of the variance of a Gaussian policy
  is often used with PPO and TRPO, e.g., in https://arxiv.org/abs/1709.06560.
  
  Args:
      action_size (int): Number of dimensions of the action space.
      var_type (str): Type of parameterization of variance. It must be
          'spherical' or 'diagonal'.
      var_func (callable): Callable that computes the variance from the var
          parameter. It should always return positive values.
      var_param_init (float): Initial value the var parameter.
docstr: Return a Gaussian with given mean.
  
  Args:
      mean (chainer.Variable or ndarray): Mean of Gaussian.
  
  Returns:
      chainerrl.distribution.Distribution: Gaussian whose mean is the
          mean argument and whose variance is computed from the parameter
          of this link.
docstr_tok: ["Return","a","Gaussian","with","given","mean",".","Args",":","mean","(","chainer.Variable","or","ndarray",")",":","Mean","of","Gaussian",".","Returns",":","chainerrl.distribution.Distribution",":","Gaussian","whose","mean","is","the","mean","argument","and","whose","variance","is","computed","from","the","parameter","of","this","link","."]
code: def __call__(self, mean):
      """Return a Gaussian with given mean.
  
      Args:
          mean (chainer.Variable or ndarray): Mean of Gaussian.
  
      Returns:
          chainerrl.distribution.Distribution: Gaussian whose mean is the
              mean argument and whose variance is computed from the parameter
              of this link.
      """
      var = F.broadcast_to(self.var_func(self.var_param), mean.shape)
      return distribution.GaussianDistribution(mean, var)
  
imports: from logging import getLogger
  import chainer
  from chainer import functions
  from chainer.initializers import LeCunNormal
  from chainer import links
  import numpy
  from chainerrl import distribution
  from chainerrl.functions.bound_by_tanh import bound_by_tanh
  from chainerrl import links
  from chainerrl.policy import Policy
label_desc: Use image node_id 2 for calling the GaussianHeadWithStateIndependentCovariance obj's underlying member method code with example usage: obj.__call__(mean) and returns: distribution